Research ArticleIncremental Value of Genotype Bins over the HAS-BLEDScore for the Prediction of Bleeding Risk in Warfarin-TreatedPatients with Atrial Fibrillation

Jia Liu ,1 Guanyun Wang ,1 Liu’an Qin ,1 Yangxun Wu ,1 Yuting Zou ,1

Xuyun Wang ,2 Ziqian Wang ,1 Yuyan Wang ,1 Shizhao Zhang ,1 Yuxiao Zhang ,2

and Tong Yin 1

1Institute of Geriatrics, National Clinical Research Center for Geriatric Diseases, 2nd Medical Center,Medical School of Chinese PLA and Chinese PLA General Hospital, Beijing 10053, China2Department of Cardiology, 1st Medical Center, Medical School of Chinese PLA, Beijing 10053, China

Correspondence should be addressed to Tong Yin; yintong2000@yahoo.com

Received 23 April 2021; Revised 25 September 2021; Accepted 26 October 2021; Published 23 November 2021

Academic Editor: Eduard Guasch

Copyright © 2021 Jia Liu et al. *is is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. *is study aimed to analyse the role of the HAS-BLED score with the addition of genotype bins for bleeding riskprediction in warfarin-treated patients with atrial fibrillation (AF). Methods and Results. Consecutive patients with AF on initialwarfarin treatment were recruited. For each patient, CYP2C9∗3 and VKORC1-1639 A/G genotyping was performed to create 3genotype functional bins. *e predictive values of the HAS-BLED score with or without the addition of genotype bins werecompared. According to the carrier status of the genotype bins, the numbers of normal, sensitive, and highly sensitive respondersamong 526 patients were 64 (12.17%), 422 (80.23%), and 40 (7.60%), respectively. A highly sensitive response was independentlyassociated with clinically relevant bleeding (HR: 3.85, 95% CI: 1.88–7.91, P � 0.001) and major bleeding (HR:3.75, 95% CI:1.17–11.97, P � 0.03). With the addition of genotype bins, the performance of the HAS-BLED score for bleeding risk predictionwas significantly improved (c-statistic from 0.60 to 0.64 for clinically relevant bleeding and from 0.64 to 0.70 for major bleeding,P< 0.01). Using the integrated discriminatory, net reclassification improvement, and decision curve analysis, the HAS-BLEDscore plus genotype bins could perform better in predicting any clinically relevant bleeding than the HAS-BLED score alone.Conclusions. Genotypes have an incremental predictive value when combined with the HAS-BLED score for the prediction ofclinically relevant bleeding in warfarin-treated patients with AF.

1. Introduction

Patients with atrial fibrillation (AF) are at a high risk for strokeand systemic embolism, which could be substantially reducedwith the administration of oral anticoagulants, includingvitamin K antagonists and nonvitamin K antagonist oralanticoagulants (NOACs) [1–3]. As a vitamin K antagonist,warfarin has been widely used in the prevention and treat-ment of thromboembolic diseases. However, due to the risksof serious and fatal bleeding complications, the use of war-farin should therefore be based on a careful assessment ofboth thromboembolic and bleeding risks [4]. In comparison

with warfarin, NOACs could reduce the risk of thrombo-embolic events, with lower mortality and half the risk ofintracranial haemorrhage [3, 5–7].*e benefit of NOACs wasconfirmed to be greater in patients with poor control ofwarfarin than in those with good control [8]. *erefore, earlyprediction of the poor control of warfarin anticoagulation isvital for personalized selection between warfarin and NOACsin AF patients who need anticoagulation treatment [9]. *eHAS-BLED bleeding risk score (hypertension (systolic bloodpressure >160mmHg), abnormal liver/renal function (cre-atinine ≥200 μmol/L), stroke, bleeding history or predispo-sition, labile international normalized ratio (INR) (INR in

HindawiCardiology Research and PracticeVolume 2021, Article ID 9030005, 8 pageshttps://doi.org/10.1155/2021/9030005

range <60% of the time), elderly (>65), and concomitantdrugs/alcohol) [10, 11] is the most widely validated model inAF and non-AF populations [12]. It has been shown tooutperform CHADS2 (cardiac failure, hypertension, age,diabetes, and stroke (double)) or CHA2DS2-VASc in pre-dicting bleeding risk [13]. Further validation studies haveconfirmed that the HAS-BLED score is an effective predictorof bleeding in warfarin-naive patients and in those admin-istered warfarin plus aspirin or nonwarfarin treatment[14, 15]. *e estimation of bleeding risk in AF patients un-dergoing anticoagulation therapy has been recommended bycurrent guidelines [3, 16–18]. However, the predictive ca-pacity of the HAS-BLED score for bleeding risk in warfarin-anticoagulated patients remains modest even with the in-corporation of the labile INR [14, 19].

In recent decades, strong evidence has shown that thegenotypes of two genes encoding the major enzyme for thewarfarin metabolism (cytochrome P4502C9 and CYP2C9)and the target protein of warfarin (vitamin K epoxide re-ductase, VKORC1) are clearly associated with the variabilityof warfarin dose and sensitivity (indicating increased risk ofbleeding under warfarin treatment) [20–22]. Specifically, themechanisms behind the association showed that theCYP2C9∗2 and ∗3 genotypes result in reduced metabolism ofthe highly active warfarin [23], and the VKORC1–1639G to Avariant could alter a transcription factor binding site, whichleads to reduced levels of themolecular target of warfarin [24].

Genotype-guided warfarin dosing has been confirmed toimprove warfarin anticoagulation [25–27], including inAsian adults [28]. In addition, findings from the ENGAGEAF-TIMI 48 trial showed that patients carrying warfarin-sensitive (carriers of CYP2C9∗1∗1 +VKORC1-1639A/A orCYP2C9∗1∗3 +VKORC1-1639G/A) or highly sensitive(carriers of CYP2C9∗1∗3 +VKORC1-1639A/A,CYP2C9∗3∗3 +VKORC1-1639A/A, CYP2C9∗3∗3 +VKORC1-1639G/A, or CYP2C9∗3∗3 +VKORC1-1639G/G)genotype functional bins of CYP2C9 and VKORC1 spendmore time over-anticoagulated and have higher rates ofbleeding events than noncarriers during warfarin treatment,especially in the early period after the start of treatment [29].*e trial also indicated that the CYP2C9 and VKORC1 ge-notypes could provide a significant benefit for the identificationof patients who are predisposed to bleeding with warfarin.

Previously, a novel biomarker-based ABC bleeding riskscore has been generated and validated with superiorbleeding risk prediction capability compared to the HAS-BLED score which includes only clinical variables [30].Genotype is a kind of biomarker; therefore, we hypothesizedthat with the addition of the genotype bins related to theresponse to warfarin, the predictive efficacy of the HAS-BLED score might be improved. *e present study aimed toanalyse the role of warfarin sensitivity-related genotype binsin addition to the HAS-BLED score for the prediction ofmajor bleeding events in warfarin-treated patients.

2. Methods

2.1. Patient Recruitment. Consecutive patients with AF oninitial warfarin treatment were recruited from the Chinese

PLA General Hospital, Beijing, China, between January 2015and June 2016. *e Ethical Review Committee of the Chi-nese PLA General Hospital approved this prospective ob-servational study. Each patient provided written informedconsent in accordance with the Declaration of Helsinki. *eincluded consecutive patients with AF were over 18 yearsold, prescribed warfarin for ≥3 months, and willing toundergo genetic testing. Patients were excluded from thestudy if they were administrated with low-intensity warfarinanticoagulation. As the majority of patients were HanChinese, patients of other ethnicities were not included inthe study.

2.2. Warfarin Anticoagulation and Follow-Up. Patients whoreceived standard warfarin anticoagulation were controlledwith a target INR of 2.0–3.0. Since bleeding complicationsand switching to NOACs are more likely to occur within theinitial stage of warfarin anticoagulation, a follow-up intervalof 3 months was required for the study. Experienced phy-sicians who were unaware of the genotypes during the wholetreatment period determined the frequency of INR mea-surements and warfarin dosing. *e time in therapeuticrange (TTR) based on the measured INRs in 3 months wascalculated using the Rosendaal method [31]. An averageTTR of <60% was considered a labile INR or poor control ofwarfarin anticoagulation [14]. Patient demographic andclinical characteristic data associated with warfarin treat-ment were collected from electronic medical records, patientself-reports, and a review by a trained nurse. Stroke risk atbaseline was assessed for each patient using the CHA2DS2-VASc score [32].

*e principal safety outcome of the study was anyclinically relevant bleeding event, including major bleedingand clinically relevant nonmajor bleeding, defined accordingto the derivation and validation studies of the HAS-BLEDscore. Major bleeding events were defined as any bleedingrequiring hospitalization or causing a decrease in the hae-moglobin level of more than 2 g/L or requiring bloodtransfusion. Clinically relevant nonmajor bleeding was de-fined as that which did not meet the major bleeding criteriabut that satisfied defined criteria (including haematuria,haematemesis, repetitive epistaxis for more than 5 minutesat least twice in 24 hours, or subcutaneous haematomas ofmore than 25 cm2 if spontaneous or more than 100 cm2 ifafter trauma) [10, 14, 33]. All suspected bleeding events wereclassified by a central adjudication committee unaware of thetreatment assignments.

2.3. GenotypingAnalysis. Genotyping was undertaken usingthe Sanger sequencing method as described previously forthe detection of warfarin sensitivity-related genotypes inChinese individuals, including CYP2C9∗3 (rs1057910) andVKORC1-1639G/A (rs9923231) [34]. *e patients weregrouped according to the genotype functional bins thatcorresponded to the FDA categories for response on theupdated warfarin label: normal responders (carriers ofCYP2C9∗1∗1 +VKORC1-1639G/G, CYP2C9∗1∗1 +VKORC1-1639G/A, or CYP2C9∗1∗3 +VKORC1-1639G/

2 Cardiology Research and Practice

G), sensitive responders (carriers of CYP2C9∗1∗1+VKORC1-1639A/A or CYP2C9∗1∗3 +VKORC1-1639G/A), and highly sensitive responders (carriers ofCYP2C9∗1∗3 +VKORC1-1639A/A, CYP2C9∗3∗3 +VKORC1-1639 A/A, CYP2C9∗3∗3 +VKORC1-1639G/A, orCYP2C9∗3∗3 +VKORC1-1639G/G) [29].

2.4. HAS-BLED Score Calculation. *e HAS-BLED scorewas calculated with the addition of 1 point for each variable,including hypertension, abnormal renal function, abnormalliver function, stroke, bleeding history or predisposition,labile INR, elderly age, and concomitant drugs/alcohol.According to the current guidelines [3], a HAS-BLED scoreof ≥3 indicates that the patient is at high risk of bleeding andrequires caution and regular review.

2.5. Statistical Analysis. Continuous variables, presented asthe mean± SD, were tested for normality using the Kol-mogorov–Smirnov test. Categorical variables are presentedas percentages. *e baseline characteristics of patients re-ceiving warfarin were compared among normal, sensitive,and highly sensitive responders. We used the chi-square testto compare the bleeding outcomes between normal re-sponders and sensitive and highly sensitive responders.Multivariate logistic regression analysis was used to analysethe independent role of responders to warfarin according togenotype bins along with the HAS-BLED score in regard tomajor bleeding complications. We constructed a receiveroperating characteristic (ROC) curve to evaluate the pre-dictive performance of responders to warfarin according togenotype bins in addition to the HAS-BLED score forbleeding events. *e comparison between the performanceof the HAS-BLED score with or without the addition ofgenotypes was achieved by the calculation of the c-statistic,integrated discriminatory improvement (IDI), net reclassi-fication improvement (NRI), and decision curve analysis(DCA). *e c-statistic was calculated by ROC analysis. IDIand NRI were performed using the “PredictABEL” packages.DCA was performed using the “rmda”and “ggDCA”packages. All calculations were performed with SPSS version22.0 (SPSS Inc, Chicago, Illinois), MedCalc v. 18.2.1(MedCalc Software bvba, Ostend, Belgium), and R Softwarev.3.2.5 (R Foundation for Statistical Computing, Vienna,Austria).

3. Results

3.1. Baseline Demographic and Clinical Characteristics. Atotal of 584 eligible patients with AF who were treated withwarfarin were enrolled in the study. Of these, 17 patientswere excluded due to the alternative administration ofNOACs within 3 months, 38 were excluded due to loss offollow-up, and 3 were excluded due to death within 3months. Finally, a total of 526 patients with AF who were onwarfarin were recruited; among them, 290 (55.13%) weremale, and the mean age was 60.63± 11.05 years. *e averagestable therapeutic dose of warfarin was 2.89± 1.06 (range:0.625–8mg/day), and 143 (27.18%) patients had a TTR of

<60%within the 3-month follow-up.*e average CHA2DS2-VASc score was 2.22± 1.53 (range: 0–8), and the averageHAS-BLED score was 1.57± 1.20 (range: 0–7). *e baselinedemographic and clinical characteristics of the includedpatients are given in Table 1. TTR is related to the quality ofwarfarin anticoagulation and is an important index; theaverage TTR values are given in Table 1.

3.2. Contribution of Genotype Bins and HAS-BLED Score toBleeding Events. All patients were successfully genotyped,with 64 (12.17%) categorized as normal responders, 422(80.23%) as sensitive responders, and 40 (7.60%) as highlysensitive responders to warfarin according to the genotypebins of CY2C9∗3 and VKORC1-1639A>G. During the 3-month follow-up, any clinically relevant bleeding eventoccurred in 67 (12.73%) patients, with major bleeding eventsoccurring in 18 (3.42%) patients. Highly sensitive re-sponders experienced significantly higher rates of bothclinically relevant bleeding (32.5%) and major bleeding(10%) than sensitive (11.35% for clinically relevant and3.08% for major) and normal responders (9.37% for clini-cally relevant and 1.56% for major) (Table 2, Figure 1).Compared with patients who had a HAS-BLED score of 0–2,patients with a HAS-BLED score of ≥3 had a significantlyincreased risk of major bleeding and any clinically relevantbleeding (Table 2; Figure 1). After multiple logistic regres-sion analysis, both highly sensitive responders and the HAS-BLED score were independent risk factors for majorbleeding (HR: 3.75, 95% CI: 1.17–11.97, P � 0.001; HR:3.93,95% CI: 1.52–10.16, P � 0.005) and any clinically relevantbleeding (HR: 3.85, 95% CI: 1.88–7.91, P � 0.001; HR:2.75,95% CI: 1.60–4.74, P � 0.001) (Table 3).

3.3. Performance of the HAS-BLED Score Plus Genotype Binsfor Bleeding Risk Prediction. In patients with both a highlysensitive response and a HAS-BLED score ≥3, the risksincreased for both clinically relevant bleeding (HR:3.39, 95%CI: 2.01–5.73, P � 0.001) and major bleeding (HR: 5.53, 95%CI: 2.03–15.01, P � 0.001) (Table 3). With the addition of ahighly sensitive response to the HAS-BLED score, the ROCcurves showed an improved ability for the discrimination ofall clinically relevant bleeding (c-statistic from 0.60 to 0.64,P � 0.01), especially the major bleeding risk (c-statistic:from 0.64 to 0.70, P< 0.001) (Figure 2). Based on the an-alyses of IDI and NRI, the HAS-BLED score plus genotypebins showed an increasing sensitivity (3.35%, P � 0.001) anda significant positive reclassification (55.68%; P � 0.003) forany clinically relevant bleeding (Table 4). Using DCA, theaddition of genotype bins to the HAS-BLED score showed abetter clinical benefit than that from the HAS-BLED scorealone (Figure 3).

4. Discussion

In the present study, we found that the genotype functionalbins of CYP2C9∗3 and VKORC1-1639 A>G provided onthe FDA warfarin label [35] affect bleeding outcomes inwarfarin-treated patients with AF. *e model with the

Cardiology Research and Practice 3

combination of a genotype bin (highly sensitive responder)and the HAS-BLED score yielded a significantly betterprognostic performance than the HAS-BLED score alone.*e results of the current study are consistent with those of aprevious prespecified pharmacogenetic study based on therandomized, double-blind trial of ENGAGE AF-TIMI 48[29]. *is result suggested that clinical and genetic data-based bleeding risk stratification has the potential for per-sonalized anticoagulation in warfarin-treated patients withAF.

*e HAS-BLED score is a relatively simple tool inte-grating clinical predictors to estimate the bleeding risk inpatients with AF receiving anticoagulation therapy [10].However, the predictive performance is moderate in war-farin-treated patients [14, 19, 36, 37], which indicates thatadditional risk factors could contribute to bleeding com-plications. We found that the genotype bin-defined highlysensitive response played an independent role in the risk ofclinically relevant bleeding. Patients with both a HAS-BLEDscore ≥3 and a genetic status of highly sensitive responder

Table 1: Baseline demographic and clinical characteristics.

Baseline characteristics Normal responder (n� 64) Sensitive responder (n� 422) Highly sensitive responder (n� 40) PMale sex, n (%) 37 (57.81%) 230 (54.50%) 22 (55%) 0.88Age, mean± SD 58.91± 9.94 60.94± 11.08 60.52± 12.33 0.41ComorbiditiesHypertension, n (%) 27 (42.18%) 168 (39.81%) 14 (35%) 0.76Hypercholesterolemia, n (%) 13 (20.31%) 55 (13.03%) 6 (15%) 0.30Diabetes, n (%) 10 (15.62%) 48 (11.37%) 7 (17.5%) 0.38Previous stroke, n (%) 12 (18.75%) 60 (14.22%) 3 (7.5%) 0.28Coronary artery disease, n (%) 23 (35.94%) 100 (23.70%) 11 (27.5%) 0.11Peripheral arterial disease, n (%) 7 (10.94%) 64 (15.17%) 3 (7.5%) 0.31Hepatic impairment, n (%) 2 (3.12%) 22 (5.21%) 2 (5%) 0.61Renal impairment, n (%) 10 (15.62%) 32 (7.58%) 5 (12.5%) 0.08Valvular heart disease, n (%) 28 (43.75%) 180 (42.65%) 17 (42.5%) 0.99Tobacco, n (%) 8 (12.5%) 76 (18.01%) 7 (17.5%) 0.58Alcoholic, n (%) 8 (12.5%) 63 (14.93%) 6 (15%) 0.88

Medications during follow-upAmiodarone, n (%) 15 (23.44%) 87 (20.62%) 8 (20%) 0.87Antiplatelets, n (%) 7 (10.94%) 25 (5.92%) 2 (5%) 0.30Digoxin, n (%) 20 (31.25%) 123 (29.15%) 9 (22.5%) 0.61Beta-blockers, n (%) 21 (32.81%) 112 (26.54%) 9 (22.5%) 0.46Calcium channel blockers, n (%) 18 (28.12%) 93 (22.04%) 10 (25%) 0.53

CHA2DS2-VASc score, mean± SD 2.28± 1.74 2.23± 1.52 1.93± 1.27 0.45HAS-BLED score, mean± SD 1.70± 1.23 1.57± 1.19 1.38± 1.25 0.40Average TTR 64.04± 19.42 69.88± 18.18 66.92± 18.89 0.22∗SD, standard deviation; TTR, time in therapeutic range.

Table 2: Bleeding events according to the HAS-BLED score and genotype bins defining warfarin response.

All patients (n� 526)Any clinical relevantbleeding event (n� 67)

Major bleeding event(n� 18)

n (%) P n (%) P

HAS-BLED score0 102 (19.39) 9 (8.82) 2 (1.96)1 177 (33.65) 18 (10.17) 4 (2.26)2 135 (25.67) 14 (10.37) 3 (2.22)3 82 (15.59) 16 (19.51) 6 (7.32)≥4 30 (5.70) 10 (33.33) ∗0.001 3 (10) ∗0.06HAS-BLED score <3 414 (78.71) 41 (9.90) 9 (2.17)HAS-BLED score ≥3 112 (21.29) 26 (23.21) #0.0003 9 (8.04) #0.005

Genotype bins defining warfarin responseNormal responder 64 (12.17) 6 (9.37) 1 (1.56)Sensitive responder 422 (80.23) 48 (11.37) 13 (3.08)Highly sensitive responder 40 (7.60) 13 (32.5) §0.0002 4 (10.00) §0.03

∗P value indicating the comparison of the bleeding incidence rate among the different accumulated points of the HAS-BLED score. #Comparison betweenpatients with a HAS-BLED score <3 and patients with a HAS-BLED score ≥3. §Comparison among patients with different genotype bins defining warfarinresponse (normal responders (carriers of CYP2C9∗1∗1 +VKORC1-1639G/G, CYP2C9∗1∗1 +VKORC1-1639G/A, or CYP2C9∗1∗3 +VKORC1-1639G/G),sensitive responders (carriers of CYP2C9 ∗1∗1 +VKORC1-1639A/A or CYP2C9 ∗1∗3 +VKORC1-1639G/A), and highly sensitive responders (carriers ofCYP2C9 ∗1∗3 +VKORC1-1639A/A, CYP2C9∗3∗3 +VKORC1-1639 A/A, CYP2C9∗3∗3 +VKORC1-1639G/A, or CYP2C9∗3∗3 +VKORC1-1639G/G).

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0

9.80% 8.23%

HAS-BLED score <3

25.00%

7.69%

22.34%

HAS-BLED score ≥3Groups

Any

clin

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HAS-BLED score <3

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HAS-BLED score ≥3Groups

Mao

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20.00%

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Normal responderSensitive responderHighly sensitive responder

Figure 1: Bleeding events according to the genotype bins defining warfarin response and the HAS-BLED score. Highly sensitive responderswere defined as carriers of the genotype bins CYP2C9 ∗1∗3 +VKORC1-1639A/A, CYP2C9∗3∗3 +VKORC1-1639 A/A,CYP2C9∗3∗3 +VKORC1-1639G/A, or CYP2C9∗3∗3 +VKORC1-1639G/G.

Table 3: Independent contribution of the HAS-BLED score and genotype bins defining warfarin response to bleeding events.

VariableAny clinically relevant bleeding Major bleeding

Hazard ratio (95% CI) aAdjusted P Hazard ratio (95% CI) aAdjusted P

HAS-BLED score ≥3 2.75 (1.60–4.74) 0.001 3.93 (1.52–10.16) 0.005Highly sensitive responder 3.85 (1.88–7.91) 0.001 3.75 (1.17–11.97) 0.03HAS-BLED score ≥3 plus highly sensitive responder 3.39 (2.01–5.73) 0.001 5.53 (2.03–15.01) 0.001Highly sensitive responders were defined as carriers of the genotype bins CYP2C9 ∗1∗3 +VKORC1-1639A/A, CYP2C9∗3∗3 +VKORC1-1639 A/A,CYP2C9∗3∗3 +VKORC1-1639G/A, or CYP2C9 ∗3∗3 +VKORC1-1639G/G. aAdjusted for age, sex, alcohol intake, aimed INRs, concomitant medications,and comorbidity conditions.

0.00.0 0.2 0.4 0.6 0.8 1.0

1 – specificity

Sens

itivi

ty

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0.4

0.6

0.8

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Sens

itivi

ty

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0.4

0.6

0.8

1.0

HAS-BLED + genotype binsHAS-BLEDReference line

C-index0.700.64

(b)

Figure 2: Receiver operating characteristic curves of the HAS-BLED score with genotype bins defining highly sensitive responders for theprediction of bleeding events. HAS-BLED score plus genotype bins indicates the addition of the genotype bins related to highly sensitiveresponders (CYP2C9 ∗1∗3 +VKORC1-1639A/A, CYP2C9∗3∗3 +VKORC1-1639 A/A, CYP2C9∗3∗3 +VKORC1-1639G/A, orCYP2C9∗3∗3 +VKORC1-1639G/G) to HAS-BLED score ≥3. (a) Any clinically relevant bleeding. (b) Major bleeding.

Cardiology Research and Practice 5

would have an approximately 6-fold increased risk of majorbleeding when treated with warfarin. As suggested, NOACswould be a better treatment for these patients, since com-pared with warfarin, NOACs were associated with a greaterreduction in bleeding risk [29]. *erefore, it is reasonable tocombine the HAS-BLED score with a highly sensitive re-sponse for the prediction of bleeding risk in warfarin-treatedpatients. Regarding the clinical implications, the new modelcould facilitate decision-making by discriminating AF pa-tients with a high risk for clinically relevant bleeding (HAS-BLED score ≥3 and highly sensitive responders) on warfarintreatment who would be better administered NOACs toachieve acceptable anticoagulation control. In view of thebroad adoption of NOACs in clinical practice, warfarin

continues to play a role in patients with contraindications orlack of access to NOACs. Moreover, warfarin is currently themain treatment with established safety in AF patients withrheumatic mitral valve disease and/or an artificial heart valve[3]. In such circumstances, warfarin-sensitive genotype binsare valuable for the selection of patients who might besuitable for warfarin treatment or whomight derive a greatersafety benefit from NOACs than warfarin.

Several limitations should be mentioned in regard to thepresent study. First, the present study was a single-centreobservational study, whichmay not be fully representative of allclinical practice. Further external validation in broader andethnically diverse patients is needed to assure the generalizationof the results. Second, the present study was not powerfulenough to observe an improvement of the new model com-pared to the HAS-BLED score, with the DCA showing only arelatively small improvement of clinical benefit. *erefore, toconfirm the clinical value of genotype bins, further large-scalestudies should be carried out to explore the contribution andcost-effectiveness of warfarin-sensitive genotype bins plus theHAS-BLED score. *ird, the hypothesis that with the additionof genotypes, the HAS-BLED score would be implicated in thepersonalized selection of VKA or NOACs in AF patients re-mains unproven until it can be validated prospectively in awell-designed randomized clinical trial.

In conclusion, the warfarin highly sensitive respondergenotype bin could moderately improve the performance ofthe HAS-BLED score for the prediction of bleeding com-plications in warfarin-treated Chinese patients with AF.

Data Availability

*e data used to support the findings of this study areavailable from the corresponding author upon request.

Disclosure

Liu J and Wang GY are the co-first authors.

Conflicts of Interest

*e authors declare that they have no conflicts of interest.

Authors’ Contributions

Liu J and Wang GY contributed equally to this work.

Table 4: C-indices of the ROC curves, ROC curve comparison, IDI and NRI of the HAS-BLED score plus genotype bins defining warfarinresponse compared to the HAS-BLED score.

C-index 95% CI P P∗ IDI (%) P NRI (%) P

Any clinically relevant bleedingHAS-BLED score plus genotype bins 0.64 0.60–0.69 <0.001 0.03 3.35 0.001 55.68 <0.001HAS-BLED score 0.60 0.56–0.64 0.001

Major bleedingHAS-BLED score plus genotype bins 0.70 0.66–0.74 <0.001 0.21 1.43 0.210 30.30 0.130HAS-BLED score 0.64 0.61–0.69 0.020

HAS-BLED score plus genotype bins indicate the addition of the genotype bins related to highly sensitive responder (CYP2C9∗1∗3 +VKORC1-1639A/A,CYP2C9∗3∗3 +VKORC1-1639A/A, CYP2C9∗3∗3 +VKORC1-1639G/A, or CYP2C9∗3∗3 +VKORC1-1639G/G) to HAS-BLED score ≥3. CI, confidenceinterval; IDI, integrated discriminatory improvement; NRI, net reclassification improvement. ∗For c-index comparison.

-5

0 10 20 30 40 50 60 70Threshold probability for any clinically relevant bleeding (%)

Net

ben

efit

(%)

0

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10

Assume any clinically relevant bleedingAssume none clinically relevant bleedingHAS-BLED scoreHAS-BLED score plus genotype bins

Figure 3: Decision curve analyses (DCAs) of the HAS-BLED scorealone and the HAS-BLED score combined with genotype bins. *isanalysis shows the clinical usefulness of theHAS-BLED score aloneand the HAS-BLED score combined with genotype bins based on acontinuum of potential thresholds for all clinically relevantbleeding events (x-axis) and the net benefit of using the model tostratify AF patients at risk (y-axis) relative to assuming that none ofthe patients will have a clinically relevant bleeding event. *is DCAevaluated the clinical usefulness of the HAS-BLED score combinedwith genotype bins for all clinically relevant bleeding events in realpractice. *e use of the HAS-BLED score combined with genotypebins implied a better net benefit than the HAS-BLED score alone.

6 Cardiology Research and Practice

Acknowledgments

*is work was supported by grants from the NationalNatural Science Foundation of China (81870262 and82170352).

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